Binary Granular Neural Network Research And Its Application In Fault Diagnosis

With the development of information technology, the acquired data are sharply increasing, which own the features as high dimension and multiple target classes. It must face for us how to mine the useful information from the mass data. Artificial neural networks are good at find the specific patterns in data, however it cannot know which features are redundant and which are key. On the other hand, GrC latest proposed by T. Y. Lin can get rid of tedious and unimportant details and grasp the essence in data. It solves problem from proper granule levels, which reduces computational complexity and obtains satisfactory solutions to the problem rapidly. It belongs to the preceding research which combines GrC and ANN.Fault diagnosis can be regarded as such procedure that mines the key features from the fault data and then finds out the fault patterns hidden in them. This dissertation took GrC as the front-end processor of ANN and set up a framework, namely a binary granular neural network. Under the framework, a binary granular neural network classifier, BGNNC in short, has been modeled and used to solve the mechanical fault diagnosis problem. The achievements in the dissertation add new connotation both to GrC theory and ANN theory, expand application regions of GrC and provide a new idea for fault diagnosis in industry field. The research contents of the dissertation belong to the crossing field of information science, automation science and computer science et al.The main innovative works in the dissertation include:(1) Designed a granular computing-based binary discernibility matrix attribute reduction algorithm. A granular layer viewpoint was introduced into the binary discernibility matrix. It took the different collums combinations as the different granular layers. The jump between different granular layers was realized by combining different collums. Aiming at the problems which the model parameters of superheater vary with the change of working parameters, the proposed algorithm was used to mine the relation and rules between working parameters and model parameters so as to build the model of the superheater. The simulation results show that the proposed algorithm has less calculation, extracts the ruls fast and is easy to implementation with programme.(2) Proposed a granular hybrid system-based control method. The core of it was the definition of granular hybrid automata. Inverted pendulum system is a nonlinear unstable system. According to its strong coupling, the swing and stabilizing procedure can be divied into three granular worlds. The threshold conditions of jumping between different granular worlds were given. It can fulfil the stability control of the inverted pendulum through the jump among the three granular worlds. The control simulation results indicated that the proposed control strategy can swing the inverted pendulum and stabilize it at the goal position in a short time.(3) Presented a binary granular matrix-based attribute reduction algorithm (BGMAR in short). BGMAR algorithm consists of two sub-algorithms which are binary granular matrix-based universe reduction (BGM-UR) and binary granular matrix-based minimum reduction (BGM-MR). Due to the repeated or contradictory objects in the universe, the BGM-UR algorithm can delete such objects and shrank the sample space where the BGM-MR will work on. Aiming that the most existing reduction algorithms cannot find the minimum attributes set, the BGM-MR find the core by calculating the wightness of each condition attribute and exploits the dependency degree as heuristic information to search the minimum reduction. The BGMAR algorithm is evaluated effective by two examples. It concludes that BGMAR algorithm is suitable not only to consistent decision table but also to inconsistent decision table, which can get the minimum reduction for both.(4) Built a framework of binary granular neural network. It takes GrC as the front-end processor of ANN. A binary granular neural network classifier (BGNNC) was proposed under the framework. It was defined as a septuple. BGNNC algorithm reduces the feature space and can find the key features, and then uses the additional momentum adaptive learning rate adjustment to train BGNNC, finally realizes the pattern classification. Breast Cancer Wisconsin (Diagnostic), Iris, Wine and Zoo datasets from UCI database are chosen to test the effectiveness of BGNNC algorithm. The results were compared with standard BP classification algorithm and shew that BGNNC has higher classification precision and better generalization than standard BP algorithm.(5) Applied the BGNNC algorithm to solve the mechanical fault diagnosis problem. In light of respective fault mechanism of the internal combustion engine (as the example of reciprocating machine) and the rolling bearing (as the example part of rotating machine), BGNNC was applied to diagnose the faults in them. The contrast experiment was designed to compare BGNNC algorithm with BP algorithm and Immune Population Network algorithm. The experiment results indicated that BGNNC has fast training speed and high diagnosis accuracy. It is proved that BGNNC is a feasible and efficient intelligent diagnosis method.